论文标题
重新聚焦事件融合的多事件相机深度估计和异常值拒绝
Multi-Event-Camera Depth Estimation and Outlier Rejection by Refocused Events Fusion
论文作者
论文摘要
事件摄像机是由生物启发的传感器，比传统摄像机具有优势。它们异步运行，以微秒的分辨率采样场景并产生亮度变化。这种非常规的输出引发了新颖的计算机视觉方法，以解锁相机的潜力。在这里，考虑了基于事件的立体声3D重建的问题。大多数基于事件的立体声方法都试图利用相机的高时间分辨率以及跨相机的事件的同时性，以建立匹配和估计深度。相比之下，这项工作研究了如何通过融合差异空间图像（DSIS）来估算深度，该研究源于有效的单眼方法。融合理论是开发并应用于设计产生最先进结果的多相机3D重建算法，这是通过与各种可用数据集的四种基线方法和测试的比较确认的。
Event cameras are bio-inspired sensors that offer advantages over traditional cameras. They operate asynchronously, sampling the scene at microsecond resolution and producing a stream of brightness changes. This unconventional output has sparked novel computer vision methods to unlock the camera's potential. Here, the problem of event-based stereo 3D reconstruction for SLAM is considered. Most event-based stereo methods attempt to exploit the high temporal resolution of the camera and the simultaneity of events across cameras to establish matches and estimate depth. By contrast, this work investigates how to estimate depth without explicit data association by fusing Disparity Space Images (DSIs) originated in efficient monocular methods. Fusion theory is developed and applied to design multi-camera 3D reconstruction algorithms that produce state-of-the-art results, as confirmed by comparisons with four baseline methods and tests on a variety of available datasets.
